Curating Narrative Experiences Through Automated Content Compilation

ABSTRACT

A content compilation system includes a computing platform having a hardware processor and a memory storing a software code configured to provide an editorial interface. The hardware processor executes the software code to receive compilation authoring data via the editorial interface, identify one or more end-user(s) for receiving a content compilation, access a consumption profile of the end-user(s), obtain, using the consumption profile and a first authoring criterion in the compilation authoring data, content items from one or more content sources. The software code further aggregates, using a second authoring criterion in the compilation authoring data, the content items into content subsets, groups, using a third authoring criterion, at least some of the content subsets to produce the content compilation, computes a desirability score predicting the desirability of the content compilation to the end-user(s), and provides, when the desirability score satisfies a predetermined threshold, the content compilation to the end-user(s).

BACKGROUND

Digital media content depicting sports, news, movies, television (TV)programming, print media, and music, for example, is consistently soughtout and enjoyed by consumers. Due to its popularity with consumers, evermore digital media content is being produced and made available fordistribution, so much so in fact that the availability of new, topical,content far exceeds the capacity for even the most ardent consumers todiscover and evaluate.

One conventional approach to making new content easier for a consumer tobecome aware of is the use of synopses, either brief text descriptionsor visual cues, such as thumbnails, for consumers to review. Whileuseful, these synopses typically describe items of content in isolation,and fail to provide any guidance with respect to other items of relatedor complementary content. Moreover, as a result of the continualproliferation of new content, the individual content items that might becombined to present related subject matter in a more entertaining orinformative light are too numerous and too varied to be aggregated andreviewed by a human consumer, or even a trained human editor. Due to theresources often devoted to developing new content, the efficiency andeffectiveness with which collections of content likely to be desirableto consumers can be introduced to those consumers has becomeincreasingly important to the producers, owners, and distributors ofdigital media content.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary content compilation system, according to oneimplementation;

FIG. 2A shows a content compilation authoring pane of an editorialinterface provided by the content compilation system shown in FIG. 1 ,according to one implementation;

FIG. 2B shows a content subset authoring pane of the editorial interfaceshown in FIG. 2A, according to one implementation;

FIG. 2C shows a content item selection pane of the editorial interfaceshown in FIGS. 2A and 2B, according to one implementation;

FIG. 3 shows a flowchart presenting an exemplary method for curating anarrative experience through automated content compilation, according toone implementation; and

FIG. 4 shows a flowchart presenting exemplary actions for extending themethod presented in FIG. 3 .

DETAILED DESCRIPTION

The following description contains specific information pertaining toimplementations in the present disclosure. One skilled in the art willrecognize that the present disclosure may be implemented in a mannerdifferent from that specifically discussed herein. The drawings in thepresent application and their accompanying detailed description aredirected to merely exemplary implementations. Unless noted otherwise,like or corresponding elements among the figures may be indicated bylike or corresponding reference numerals. Moreover, the drawings andillustrations in the present application are generally not to scale, andare not intended to correspond to actual relative dimensions.

The present application discloses systems and methods for curatingnarrative experiences through automated content compilations thataddress and overcome the deficiencies in the conventional art. Anautomated content compilation system according to the present novel andinventive concepts produces a content compilation for one or moreend-users (hereinafter “end-user(s)”), and computes a desirability scorepredicting the desirability of the content compilation to theend-user(s). The automated content compilation is performed by thesystem based on a consumption profile of the end-user(s) and eithercontent authoring data received through an editorial interface from ahuman editor other than the end-user(s), or based on a compilationauthoring template generated by a trained machine learning model of thesystem. When the desirability score for the content compilation producedby the present system satisfies a predetermined threshold, the contentcompilation may be pushed or otherwise provided to the end-user(s) suasponte, i.e., proactively by the system, rather than in response to arequest for the content compilation from the end user. Moreover, thedisclosed system can further receive feedback data rating an actualdesirability of the content compilation to the end-user(s), and modify,using the trained machine learning model and the feedback data, acompilation authoring template used by the system to advantageouslyimprove the automated performance by the content compilation system inthe future.

It is noted that, as used in the present application, the terms“automation.” “automated,” and “automating” refer to systems andprocesses that do not require the participation of a human editor orcurator. Although, in some implementations, a human editor or curatormay review a content compilation produced by the automated systems andaccording to the automated methods described herein, that humaninvolvement is optional. Thus, the methods described in the presentapplication may be performed under the control of hardware processingcomponents of the disclosed automated systems.

It is further noted that although the present application is titled“Curating Narrative Experiences through Automated Content Compilation.”that characterization is provided as an aid to conceptual clarity and isnot to be construed as limiting. The present concepts can be applied todigital media content in a variety of forms, including audio-videocontent, video content without audio, audio content without video, suchas music for instance, still images, graphics, such as sports or weatherstatistics, and text, to name some examples. Consequently, the term“narrative,” as defined for the purposes of the present application,refers to digital media content drawn from one or more of the types ofcontent described above, and is not limited to speech or text. Moreover,the present application contemplates a content compilation as includinga hierarchy of content combinations in which individual content items inthe form of video clips, news items, still images, graphics, or samplesof audio tracks, for example, are aggregated into content subsets eachrelating a consistent “narrative” story, based on their relevance to oneanother and a known end-user preference. Those content subsets or“stories” may then be grouped, such as by being strung together, i.e.,concatenated, to produce a content compilation or “narrative experience”having a narrative arc determined by the sequence in which the contentsubsets are combined to form the content compilation.

It is noted that, as used in the present application, the expression“narrative experience” refers to a customized compilation of videoclips, graphics, audio, dynamically generated text or other creativeelements that are relevant to and individual end-user or a group ofend-users. As one non-limiting example, a narrative experience may takethe form of a personalized sports news show, featuring a compilation ofhighlight clips relevant to the players and teams preferred by theend-users), logos relevant to the teams and leagues preferred by theend-user(s), and dynamically generated audio and motion graphicscapturing the sports news stories most relevant to the preferences ofthe end-user(s) and driven by other parameters, such as recency forinstance. As another non-limiting example, a narrative experience maytake the form of a marketing video for a theme park, featuring videofootage and photos of the end-user(s) captured on favorite theme parkrides from past visits, stock footage of the resort where theend-user(s) stayed and the parks they visited, and dynamically generatedtext and motion graphics capturing dates, locations, and other detailsof the visit.

FIG. 1 shows an exemplary system for curating narrative experiencesthrough automated content compilation, according to one implementation.As shown in FIG. 1 , content compilation system 100 includes computingplatform 102 having hardware processor 104 and system memory 106implemented as a non-transitory storage device. According to the presentexemplary implementation, system memory 106 stores software code 110including trained machine learning model 112, editorial interface 114provided by software code 110, consumption profile database 120, andcompilation authoring database 130. Also shown in FIG. 1 are dataset 132of content authoring data, and individual end-user consumption profiles122 a, 122 b, and 122 c stored in consumption profile database 120.

As further shown in FIG. 1 , content compilation system 100 isimplemented within a use environment including communication network108, client system 136 including display 138, end-users 124 includingindividual end users 124 a, 124 b, and 124 c, human editor 126 otherthan one of end-users 124, and content sources 140 a, 140 b, and 140 c(hereinafter “content sources 140 a-140 c”). In addition. FIG. 1 showsnetwork communication links 118 of communication network 108interactively linking content compilation system 100 with client system136, content sources 140 a-140 c, and end-users 124. Also shown in FIG.1 are authoring data 128, content items 142 a. 142 b, and 142 c(hereinafter “content items 142 a-142 c”) obtained by contentcompilation system 100 from respective content sources 140 a-140 c, oneor more content compilations 144 (hereinafter “content compilation(s)144”) produced by content compilation system 100, and feedback data 146received from one or more of end-users 124 and rating the actualdesirability of content compilation(s) 144 to the end-user(s).

It is further noted that although FIG. 1 shows three content sources andthree end-users, that representation is merely exemplary. In otherimplementations, as few as one content source, or more than threecontent sources may provide content items for automated compilation bycontent compilation system 100. Moreover, end users 124 would typicallyinclude many more than three individual end-users, such as thousands,tens of thousands, or hundreds of thousands of end-users, for example.

It is also noted that although FIG. 1 shows a single client system usedby a single human editor, it is contemplated that in manyimplementations multiple client systems may be utilized by multipledifferent human editors to interact with content compilation system 100substantially concurrently. Consequently, client system 136 and humaneditor 126 correspond respectively to one, or more than one clientsystem and one, or more than one human editor.

Although FIG. 1 depicts software code 110, consumer profile database120, and compilation authoring database 130 as being stored in systemmemory 106, that representation too is merely exemplary. More generally,system memory 106 may take the form of any computer-readablenon-transitory storage medium. The expression “computer-readablenon-transitory storage medium.” as used in the present application,refers to any medium, excluding a carrier wave or other transitorysignal that provides instructions to hardware processor 104 of computingplatform 102. Thus, a computer-readable non-transitory medium maycorrespond to various types of media, such as volatile media andnon-volatile media, for example. Volatile media may include dynamicmemory, such as dynamic random access memory (dynamic RAM), whilenon-volatile memory may include optical, magnetic, or electrostaticstorage devices. Common forms of computer-readable non-transitory mediainclude, for example, optical discs. RAM, programmable read-only memory(PROM), erasable PROM (EPROM), and FLASH memory.

It is further noted that although FIG. 1 depicts software code 110,consumer profile database 120, and compilation authoring database 130 asbeing co-located in system memory 106, that representation is alsomerely provided as an aid to conceptual clarity. More generally, contentcompilation system 100 may include one or more computing platforms 102,such as computer servers for example, which may be co-located, or mayform an interactively linked but distributed system, such as acloud-based system, for instance. As a result, hardware processor 104and system memory 106 may correspond to distributed processor and memoryresources within content compilation system 100.

In some implementations, computing platform 102 may correspond to one ormore web servers, accessible over a packet-switched network such as theInternet, for example. Alternatively, computing platform 102 maycorrespond to one or more computer servers supporting a private widearea network (WAN), local area network (LAN), or included in anothertype of limited distribution or private network. Consequently, in someimplementations, software code 110, consumer profile database 120, andcompilation authoring database 130 may be stored remotely from oneanother on the distributed memory resources of content compilationsystem 100.

Although client system 136 is shown as a desktop computer in FIG. 1 ,that representation is also provided merely as an example. Moregenerally, client system 136 may be any suitable mobile or stationarycommunication device or system that implements data processingcapabilities sufficient to support connections to communication network108, enable use of editorial interface 114, and implement thefunctionality ascribed to client system 136 herein. For example, inother implementations, client system 136 may take the form of a laptopcomputer, a tablet computer, or a smartphone, for example.

Human editor 126 may be authorized to utilize client system 136, andeditorial interface 114 rendered on display 138 of client system 136, toguide authoring of content compilations by content compilation system100. Display 138 of client system 136 may take the form of a liquidcrystal display (LCD), a light-emitting diode (LED) display, an organiclight-emitting diode (OLED) display, a quantum dot (QD) display, or anyother suitable display screen that performs a physical transformation ofsignals to light.

By way of overview, the present content compilation solution creates avirtuous circle between algorithmic content compilation by software code110, executed by hardware processor 104, and authoring data inputsprovided by human editor 126 via editorial interface 114. In someimplementations, human editor 126 can receive substantially instantfeedback on manual story creation in the form of a predicted end-userdesirability score and automated suggestions of higher scoring storycompilations put together by trained machine learning model 112 ofsoftware code 110. This advantageously enables human editor 126 todiscover content combinations that were unintuitive to human editor 126but may be desirable to one or more of end-users 124, and to learn moreeffective narrative arcs based on real-world consumption data orfeedback data 146. By capturing metadata around the human editorialauthoring process, trained machine learning model 112 can learn newstarting story templates, improve compilation scoring mechanisms, andrefine the editorial style of human editor 126 by testing and adjustingauthoring criteria, leading to more effective compilation authoringtemplates.

Compilation scoring mechanisms may be further improved based on afeedback loop utilized by trained machine learning model 112 toprogressively improve its performance. For example, weighting factorsused to compute the desirability score for a content compilation maybecome progressively more accurate due to adjustments made by trainedmachine learning model 112 in response to feedback data 146 receivedfrom end-users 124 and rating the actual desirability of the contentcompilation to a particular end-user or group of end-users. That is tosay, trained machine learning model 112 can utilize feedback data 146 toadvantageously improve the automated performance by content compilationsystem 100 in the future.

By running software code 110 on new content items as such contentbecomes available, a process is created that automatically constructshigh-value content compilations, and once the predicted desirabilityscore satisfies a predetermined threshold, the content compilation canbe pushed or otherwise provided to an individual end-user or to a groupof end-users, which advantageously eliminates the need for theend-users) to explicitly request the content compilation. Additionally,the present automated content compilation solution allows the productionand delivery of individualized content compilations that target a singleend-user or a group or end-users uniquely, rather than conventionalapproaches in which content is produced based on collective interests ofa particular demographic population.

For instance, rather than having a human editor create a genericcombination of content targeting a demographic of male basketball fansin a certain age range who live in the Los Angeles, Calif. area, thepresent solution can be implemented to produce and deliver a differentnarrative experience for each of the individuals who make up thatdemographic by mixing content relevant to other personalizedcharacteristics, preferences, future plans, and consumption patterns, aswell as by crossing outside the boundaries of a single subject area(e.g., basketball) to including related topics and affinities. This maysignificantly benefit the end-user(s) by relieving them of therequirement of finding personalized content piecemeal from multiplecontent sources.

As a specific example, the present solution enables the automatedcompilation of a narrative experience containing information relevant tothe present status of a pandemic in Florida as it relates to thevacation plans of a Los Angeles resident end-useras), weaving into thatnarrative experience information about vacation plans to visit a Floridabased theme park at that time. In addition, the content compiled intothe end-user(s) specific narrative experience may include informationregarding a sporting event involving a favorite team, a weather forecastfor that event, and any other relevant content items without theend-user(s) having to consult several applications or visit severalwebsites to gather the related but distinctively sourced contentautomatically compiled according to the present novel and inventiveconcepts.

Thus, content compilations can advantageously be produced and deliveredbased on the end user's affinities. For instance, while a generic sportshighlight package might attempt to appease fans of both teams competingin a sporting event, present content compilation solution producesnarrative experiences that are consistent with the sentiments of theend-user(s) by emphasizing content items that relate to theirpreferences while avoiding the inclusion of content that relates tocounter-preferences. This can result in the production of contentcompilations in the form of multiple highlight packages that arehyper-focused to the end-user(s), limited only by the granularity of thecontent items and specificity of the metadata as it relates topreferences.

FIG. 2A shows exemplary content compilation authoring pane 250 ofeditorial interface 214 according to one implementation. Also shown asbackground panes in FIG. 2A, are content subset authoring pane 260 andcontent item selection pane 270, each including fields that can bepopulated or modified by human editor 126 as described below, as well asend-user(s) ID field 259 identifying a particular one of end-users 124,or a particular subgroup of end-users 124 for whom contentcompilation(s) 144 being authored using editorial interface 214 is/areintended. It is noted that content compilation authoring pane 250depicts the compilation of various types of sports content for merelyexemplary purposes. As noted above, the present content compilationsolution is applicable to digital media content in a variety of forms,including audio-video content, video content without audio, audiocontent without video, such as music for instance, still images,graphics, such as sports or weather statistics, and text, to name someexamples.

According to the exemplary implementation shown in FIG. 2A, individualcontent items in the form of video clips, news items, still images,graphics, or samples of audio tracks, for example, have been aggregatedinto content subsets 260 a, 260 b, and 260 c each having a specificfocus. Thus, content subset 260 a includes a full game analysis of asporting event such as a specific collegiate or professional basketballgame, while content subset 260 b includes content that addresses thesports topic more generally, such as the top players in the running toreceive a league award for the sport analyzed in content subset 260 a.Content subset 260 c, by contrast may include video highlights of othergames played the same day, as well as interviews with key players orcoaches from other teams, for example.

As shown in FIG. 2A, content compilation authoring pane 250 may includefield 252 identifying the number of content subsets, or the number ofcontent items, available for use in producing a content compilation. Inaddition, fields 254 a and 254 b identify weighting factors applied inevaluating and organizing content subsets 260 a, 260 b, and 260 c. Forexample, using a weighting range of 1 (lowest weight) to 10 (highestweight). FIG. 2A shows that unique preference weight 254 b is mostdeterminative in the selection and organization of content subsets 260a, 260 b, and 260 c.

Unique preference weight 254 b may specify how varied content subsets260 a, 260 b, and 260 c included in one of content compilation(s) 144should be. That is to say, unique preference weight 254 b enables thescoring mechanism applied to a content compilation to take into accounthow individual content subsets 260 a, 260 b, and 260 c influence thescore of the content compilation as a whole. For example, if all contentsubsets corresponding to end-user(s) preferences have been included, aparticular content compilation will receive a higher desirability scoreif the content included in the content compilation is more varied (e.g.,a variety of different teams playing, a sports content subset followedby a weather report or news item rather than all sports clips of thesame team or featuring the same player). Alternatively, or in addition,in some implementations, unique preference weight 254 b may be used toidentify combinations of content subsets 260 a. 260 b. 260 c that arelikely to appeal to a diversity of user preferences of the end-user(s).e.g., more variation in the user preferences that trained machinelearning model 112 is matching on.

Content subsets or “stories” 260 a. 260 b, and 260 c may then begrouped, such as by being strung together. i.e., concatenated, toproduce a content compilation or “narrative experience” having anarrative arc determined by the sequence in which content subsets arecombined in the content compilation, i.e., content subset 260 a followedby content subset 260 b followed by content subset 260 c. Also shown inFIG. 2A is content subset authoring pane 260, which a human editor, suchas human editor 126 in FIG. 1 , may access by clicking through any oneof content subsets 260 a, 260 b, 260 c using input device 234, which asshown as an exemplary cursor in FIG. 2A.

In some implementations, human editor 126 can receive substantiallyinstant feedback on manual story creation in the form of a predictedend-user desirability score or automated suggestions for higher scoringstory compilations put together by trained machine learning model 112 ofsoftware code 110 through use of compilation test engine 256. Thisadvantageously enables human editor 126 to discover content combinationsthat may be unintuitive to human editor 126 but desirable to one or moreof end-users 124, and to learn more effective narrative arcs. Forexample, human editor 126 may enter a beginning data in begin date field258 a and an ending data in end date field 258 b, and compilation testengine 256 may use those beginning and end dates and combinations of twoor more of content subsets 260 a, 260 b, and 260 c to test the predicteddesirability of various content compilations for one or more ofend-users 124.

Referring to FIG. 2B, FIG. 2B shows content subset authoring pane 260 ofeditorial interface 214, according to one implementation. According tothe exemplary implementation shown in FIG. 2B, content subset authoringpane 260 is being used to aggregate content items 242 a, 242 b, and 242c to produce full game analysis content subset 260 a, as indicated bycontent subset name field 262. Also shown in FIG. 2B are fields 264 aand 264 b identifying weighting factors applied in evaluating andorganizing content items 242 a, 242 b, and 242 c. For example, using aweighting range of 1 (lowest weight) to 10 (highest weight). FIG. 2Bshows that time relevance weight 264 a is most determinative in theselection and organization of content items 242 a. 242 b, and 242 c,while topic cohesion weight 264 b corresponding to the relatedness ofcontent items 242 a. 242 b, 242 c, i.e., their direct relevance to oneanother, is less important.

FIG. 2C shows content item selection pane 270 of editorial interface214, according to one implementation. According to the exemplaryimplementation shown in FIG. 2C, content item selection pane 270 isbeing used to automate the selection of content to be included inPre-Game Analysis or Conversation content item 242 a, as indicated bycontent item name field 272. Also shown in FIG. 2C are fields 274 a, 274b, and 274 c identifying weighting factors applied in evaluating theimportance of matching a user's preference for a particular sportsleague, player, and team, respectively, when selecting content forinclusion in content item 242 a. In addition, FIG. 2C shows contentweighting fields 276 a, 276 b, and 276 c indicating the priority givento content in the form of analysis, interviews, and news content,respectively, as well as primary preference weight 278.

Regarding primary preference weight 278, it is noted that if a contentitem is determined to have a primary preference association, such as inthe case of a sporting event where one team or player is highlighted inthe clip even though multiple players and teams are involved in thecontent, primary preference weight 278 may be used to select contentwith more emphasis on a user's particular preference, e.g., preferringhighlights where a favorite player is scoring but omitting highlightswhere the player appears in the clip but is not the focal point. Inother words, primary preference weight 278 demonstrates that individualcontent items can be chosen based on the weight of individual tags asthey pertain to that specific item of content.

Editorial interface 214 in FIGS. 2A, 2B, and 2C corresponds in generalto editorial interface 114, in FIG. 1 . That is to say, editorialinterface 114 may share any of the characteristics attributed tocorresponding editorial interface 214 by the present disclosure, andvice versa. Thus, although not shown in FIG. 1 , editorial interface 114may include content item selection pane 270, content subset authoringpane 260, content compilation authoring pane 250, and any of thefeatures shown and described by reference to FIGS. 2A, 2B, and 2C. Inaddition, content items 242 a. 242 b, and 242 c correspond respectivelyin general to content items 142 a-142 c, in FIG. 1 , and thosecorresponding features may share any of the characteristics attributedto either corresponding feature by the present disclosure.

The functionality of software code 110 including trained machinelearning model will be further described by reference to FIG. 3 . FIG. 3shows flowchart 380 presenting an exemplary method for use by a system,such as content compilation system 100, in FIG. 1 , for curating anarrative experience through automated content compilation, according toone implementation. With respect to the method outlined in FIG. 3 , itis noted that certain details and features have been left out offlowchart 380 in order not to obscure the discussion of the inventivefeatures in the present application.

Referring to FIG. 3 in combination with FIGS. 1, 2A. 2B, and 2C,flowchart 380 begins with receiving compilation authoring data 128 viaeditorial interface 114/214 (action 381). Compilation authoring data 128may include some or all of the information included in field 252, inFIG. 2A, the weighting factors included in fields 254 a and 254 b, anddata entered into end-user(s) ID field 259 in that same figure, as wellas the weighting factors included in fields 264 a and 264 b, in FIG. 2B,for example.

As shown by FIG. 1 , compilation authoring data 128 may be entered intoeditorial interface 114/214 by human editor 126, and may be receivedfrom client system 136 via communication network 108 and networkcommunication links 118. Compilation authoring data 128 may be receivedby software code 110, executed by hardware processor 104 of computingplatform 102.

Flowchart 380 continues with identifying end-user(s) for receivingcontent compilation(s) 144 (action 382). In some implementations, theend-user(s) for receiving content compilation(s) 144 may be a subgroupof end-users 124, based on their shared interest in the same subjectmatter, or due to their all being fans of the same sport or sportsorganization, for example. That is to say, the end-user(s) identified inaction 144 may include a group of end-users, e.g., a subgroup ofend-users 124, such as a group of consumers, a family, a group residingin a particular geographical region, fans of a particular team, or anyother affinity group. However, in other implementations, contentcompilation(s) 144 may be individualized for delivery to a single one ofend-users 124, such as end-user 124 a, but not end-users 124 b or 124 c.

In the interests of conceptual clarity, action 382, as well as actions383-388 and the additional actions included in FIG. 4 , are furtherdescribed by reference to an exemplary use case in which contentcompilation(s) 144 is/are produced for end-user 124 a alone. Moreover,according to the exemplary use case described below, a consumptionprofile of end-user 124 is stored as end-user consumption profile 122 ain consumption profile database 120. In some implementations, end-user124 a for receiving content compilation(s) 144 may be identified inaction 382 using data included in end-user(s) ID field 259, in FIG. 2A.However, in other implementations, end-user 124 a may be identified inaction 382 through an automated back-end process that does not rely onpopulation of end-user(s) ID field 259. In those latter implementations,field 259 may be used for testing purposes, for example to assist humaneditor 126 discover new, higher scoring content compilation(s) 144, orto target groups of end-users 124 including end-user 124 a, rather thanend-user 124 a individually. End-user 124 a may be identified bysoftware code 110, executed by hardware processor 104 of computingplatform 102.

Flowchart 380 continues with accessing consumption profile 122 a ofend-user 124 a (action 383). End-user consumption profile 122 a mayinclude the content consumption history of end-user 124 a, preferencesof end-user 124 a, demographic information such as the age, gender, andgeographical location of end-user 124 a, content subscriptions held byend-user 124 a, affinity groups or loyalty programs participated in byend-user 124 a, as well as miscellaneous information entered intoend-user consumption profile 122 a by end-user 124 a, such as birthdays,travel plans, or subjects of special interest, for example. In addition,in some implementations, end-user consumption profile 122 a may includeinformation automatically detected by a communication device utilized byend-user 124 a. For example, localization data, such as GlobalPositioning System (GPS) data collected by the communication deviceutilized by end-user 124 a for instance, may be included in end-userconsumption profile 122 a and may be used to identify a present or pastlocation of end-user 124 a. Alternatively, or in addition, accelerometeror gyroscope data collected by the communication device utilized byend-user 124 a may indicate that end-user 124 a is in motion, and may bepresently physically active, commuting, traveling, or otherwise intransit.

End-user consumption profile 122 a of end-user 124 a may be accessed onconsumption profile database 120 by software code 110, executed byhardware processor 104 of computing platform 102. In implementations inwhich consumption profile database 120 and software code 110 areco-located in system memory 106, hardware processor 104 may executesoftware code 110 to access end-user consumption profile 122 a directly.However, in implementations in which consumption profile database 120 isstored remotely from software code 110, hardware processor 104 mayexecute software code 110 to access end-user consumption profile 122 avia communication network 108 and network communication links 118.

Flowchart 380 continues with obtaining, using end-user consumptionprofile 122 a and a first authoring criterion included in compilationauthoring data 128, some or all of content items 142 a/242 a, 142 b/242b, and 142 c/242 c from one or more of content sources 140 a-140 c(action 384). It is noted that although FIG. 1 depicts content item 142a/242 a being obtained from content source 140 a, content item 142 b/242b being obtained from content source 140 b, and content item 142 c/242 cbeing obtained from content source 140 c, that representation is merelyexemplary. In other implementations some or all of content items 142a/242 a, 142 b/242 b, and 142 c/242 c may be obtained from the samecontent source. e.g., one of content sources 140 a-140 c. In otherimplementations, content items 142 a/242 a, 142 b/242 b, and 142 c/242 cmay be obtained from more than one, but less than all of content sources140 a-140 c.

The first authoring criterion used in action 384 may include thedetermination of one or more of an end-user relevance score, a relativetiming score, or a consumer popularity score for each of content items142 a/242 a, 142 b/242 b, and 142 c/242 c, for example. The end-userrelevance score may be determined based on the subject matter of each ofcontent items 142 a/242 a. 142 b/242 b, and 142 c/242 c relative toknown preferences of end-user 124 a, as well as a content consumptionhistory of end-user 124 a. The relative timing score may be determinedbased on time offsets among individual content items 142 a/242 a, 142b/242 b, and 142 c/242 c. As a specific example, where content item 142b/242 b is a full game highlight clip of a particular sporting event, itmay be advantageous or desirable for content items 142 a/242 a and 142c/242 c to precede or follow the subject matter included in content item142 b/242 b in time, but not to coincide with it. Thus, content frompre-game analysis or conversations may earn a high relative timing scorefor use as content item 142 a/242 a, while post-game analysis orinterviews may earn a high relative timing score for use as content item142 c/242 c.

The consumer popularity score may be based on the number of views orhits a particular content item has received from a general consumerpopulation, or from a subgroup of consumers, such as fans of aparticular team, or consumers sharing similar demographiccharacteristics. It is noted that even in use cases in which consumptionprofile database 120 does not include a consumption history specific toend-user 124 a, collaborative filtering recommendation techniques can beused to identify content items 142 a/242 a, 142 b/242 b, and 142 c/242 cto be obtained in action 384. That is to say, in some implementations,content items 142 a/242 a, 142 b/242 b, and 142 c/242 c may beidentified and obtained by reference to a consumption profile of ademographic of end-users 124 determined to be similar to end-user 124 a.For example, content items 142 a/242 a, 142 b/242 b, and 142 c/242 c maybe identified and obtained based on information such as the age, gender,and geographical location of end-user 124 a and consumption historyinformation for end-users 124 having a similar age and geographicallocation, and the same gender. Content items 142 a/242 a, 142 b/242 b,and 142 c/242 c may be obtained from one or more of content sources 140a-140 c in action 384 by software code 110, executed by hardwareprocessor 104 of computing platform 102.

As noted above, content items 142 a/242 a, 142 b/242 b, and 142 c/242 cmay take a variety of forms. For instance, content items 142 a/242 a,142 b/242 b, and 142 c/242 c may be audio-visual content, such as amovie. TV, news, weather, or sports clip. As a result, in someimplementations, content items 142 a/242 a, 142 b/242 b, and 142 c/242 cmay be video clips. Alternatively, in some implementations, one or moreof content items 142 a/242 a, 142 b/242 b, and 142 c/242 c may be musicor other audio samples, passages quoted from digital print media, or oneor more graphical elements or overlays.

It is further noted that, in some implementations, content items 142a/242 a, 142 b/242 b, and 142 c/242 c may include content having a shortduration. For example, where content items 142 a/242 a, 142 b/242 b, and142 c/242 c are video clips, content items 142 a/242 a. 142 b/242 b, and142 c/242 c may have a playout duration of a few seconds, such asapproximately ten to fifteen seconds, for example, or may be limited toone or a few shots of video.

As used in the present application, a “shot” refers to a sequence ofvideo frames that is captured from a unique camera perspective withoutcuts and other cinematic transitions. Thus, in one implementation, oneor more of content items 142 a/242 a, 142 b/242 b, and 142 c/242 c maycorrespond to a single shot of video content including multipleindividual frames of video. However, in other implementations, one ormore of content items 142 a/242 a. 142 b/242 b, and 142 c/242 c maycorrespond to a content segment including multiple shots.

Flowchart 380 continues with aggregating, using a second authoringcriterion included in compilation authoring data 128, content items 142a/242 a, 142 b/242 b, and 142 c/242 c into multiple content subsets 260a. 260 b, 260 c (action 385). The second authoring criterion used inaction 385 may include the determination of a content compatibilityscore for each of subsets 260 a, 260 b, and 260 c. For example,referring to FIG. 2A, field 254 a includes a relatively high (i.e.,8/10) weighting factor being applied to the importance of contentincluded in content compilation being relevant to the same unique event,such as a particular sporting event. Thus, the content compatibilityscore determined as part of the second authoring criterion used inaction 385 may be higher for aggregations of content subsets 260 a, 260b, and 260 c that are topically related but not conflicting byoverlapping in time or having a sentiment counter to known preferencesof end-user 124 a.

Each content subset 260 a, 260 b, and 260 c may correspond to a storyassembled or aggregated from multiple content items drawn to the same orsimilar subject matter and theme. For example, as described above byreference to FIG. 2A, content subset 260 a may include a full gameanalysis of a sporting event such as a specific collegiate orprofessional basketball game, while content subset 260 b includescontent that addresses the sports topic more generally, such as topplayers in the running to receive a league award for the sport analyzedin content subset 260 a. Content subset 260 c, by contrast, may includevideo highlights of other games played the same day, as well asinterviews with key players or coaches from other teams, for example.Content subsets 260 a. 260 b, and 260 c may be aggregated from contentitems, such as content items 142 a/242 a. 142 b/242 b, and 142 c/242 cfor example, by software code 110, executed by hardware processor 104 ofcomputing platform 102.

Flowchart 380 continues with grouping, using a third authoring criterionincluded in compilation authoring data 128, at least some of contentsubsets 260 a. 260 b, and 260 c to produce content compilation(s) 144(action 386). The third authoring criterion used in action 386 mayinclude the determination of a content variety score for contentcompilation(s) 144. For instance, while it may be desirable in theinterests of narrative flow for content subsets 260 a. 260 b, and 260 cto be consistent with one another topically, it may also be desirable toensure that content subsets 260 a. 260 b, and 260 c address the same orsimilar topics from different perspectives, or impart different types ofinformation. As a specific example, and as noted above, contentcompilation(s) 144 may include varied information such as the presentstatus of a pandemic in Florida as it relates to the vacation plans of aLos Angeles resident end-user 124 a, as well as information about thatend-user's vacation plans to visit a Florida based theme park at thattime, weather forecast information for the upcoming trip, as well asinformation regarding a sporting event or team of special interest toend-user 124 a.

Regarding determination of the content variety score, such a score maytake into account (but is not limited to) the following features: 1) Theaggregated individual scores of content subsets 260 a, 260 b and 260 c(which could be weighted—for example, a content subset with a longerduration might have a stronger weight) and 2) the similarity oruniqueness between content subsets 260 a, 260 b and 260 c, which couldbe computed using different metadata parameters as drivers, for example,whether they contain one or more of the same “topic” or “theme” tag, atag for the same “sport.” and a unique identifier indicating that theyrelate to the same “event.” Alternatively, or in addition, the contentvariety score may take into account one or more of: 3) the aggregatedlength of the entire content compilation or the relative lengths of thecontent subsets contained therein, and 4) the aggregated recency of theentire content compilation or the relative temporal ordering of thecontent subsets contained it contains, for example whether the contentsubsets are sorted into a natural chronological order.

In addition to, or as alternatives to the content variety scoredescribed above, the third authoring criterion included in compilationauthoring data 128 may include one or more of the number of contentsubsets to be included in content compilation(s) 144, the type ofcontent subsets to be included in content compilation(s) 144 (which arein turn defined by their component videos, creative elements, recency,similarity and other parameters), the selection of individual creativeelements (such as graphical or audio transitions) to maximize continuitybetween the content subsets, and the logical ordering of content subsetsto be included in content compilation(s) 144 (such as chronological orreverse chronological order, ordering based on duration, or groupingsubsets relating to the same theme or entity together), to name a fewexamples.

Thus the number, ordering, relevance, and variety of at least some ofcontent subsets 260 a. 260 b, and 260 c included in contentcompilation(s) 144 may be used to individualize content compilation(s)144 for end-user 124 a based on end-user consumption profile 122 a ofend-user 124 a. Moreover, in some implementations, contentcompilation(s) 144 may be individualized for end-user 124 a so as to beunique to end-user 124 a. Content subsets 260 a, 260 b, and 260 c may begrouped to produce content compilation(s) 144 in action 386 by softwarecode 110, executed by hardware processor 104 of computing platform 102.

It is noted that although flowchart 380 shows each of actions 385 and386 as occurring once, and further shows action 385 as preceding action386, that representation is merely exemplary. In some implementations,one or both of actions 385 and 386 may be repeated multiple times priorto action 387 described below. Moreover, in some implementations, theorder of those multiple iterations of actions 385 and 386 may alternate.As one example, action 385 may be performed repeatedly to firstaggregate content items 142 a/242 a, 142 b/242 b, and 142 c/242 c intocontent subsets 260 a, 260 b, and 260 c, may then proceed withaggregating one or more of content subsets 260 a, 260 b, and 260 c intocontent supersets, and may further continue with aggregating thosesupersets into super-supersets prior to action 386. Alternatively, insome implementations, actions 385 and 386 may initially occur asoutlined by flowchart 380, but may be followed by repetition of action385, or action 386, or actions 385 and 386 one or more times prior toaction 387.

Flowchart 380 continues with computing a desirability score predictingthe desirability of content compilation(s) 144 to end-user 124 a (action387). In some implementations, the desirability score computed in action387 may include some or all of the scores determined in actions 384,385, and 386. Thus, the desirability score may include some or all ofthe variety score, the content compatibility score, and the one or moreof the end-user relevance score, the relative timing score, or theconsumer popularity score described above.

In some implementations, the desirability score computed in action 387may be an unweighted sum of the scores determined in action 384, 385,and 386. In other implementations, however, the desirability scorecomputed in action 387 may be a weighted sum (which may include negativeas well as positive weighting factors) of the scores determined inaction 384, 385, and 386. That is to say, in some implementations, thedesirability score predicting the desirability of content compilation(s)144 to end-user 124 a may be higher than, or lower than, the sum of thevariety score, the content compatibility score, and the one or more ofthe end-user relevance score, the relative timing score, or the consumerpopularity score. Computation of the desirability score in action 387may be performed by software code 110, executed by hardware processor104 of computing platform 102.

Flowchart 380 can conclude with providing, when the desirability scorecomputed in action 387 satisfies a predetermined threshold, contentcompilation(s) 144 to end-user 124 a (action 388). In oneimplementation, providing content compilation(s) 144 to end-user 124 amay include pushing content compilation(s) 144 to end user 124 a overcommunication network 108 and network communication links 118. That isto say, content compilation system 100 may provide end-user 124 a with apush notification informing end-user 124 a that content compilation(s)144 is/are available. Hardware processor 104 may then execute softwarecode 110 to stream content compilation(s) 144 to end-user 124 a viacommunication network 108 and network communication links 118.Conditioning action 388 on the desirability score of contentcompilation(s) 144 satisfying a predetermined threshold advantageouslyprevents providing content to end-user 124 a that end-user 124 a mayfind irrelevant or otherwise undesirable.

Referring to FIG. 4 , FIG. 4 shows flowchart 490 presenting exemplaryactions for extending the method outlined by flowchart 380, in FIG. 3 .With respect to the actions listed in FIG. 4 , it is noted that certaindetails and features have been left out of flowchart 490 in order not toobscure the discussion of the inventive features in the presentapplication.

Flowchart 490 begins with obtaining dataset 132 of compilation authoringdata generated by human editor 126 (action 491). Dataset 132 may includemultiple instances of compilation authoring data 128 utilized by humaneditor 126 to generate content compilation(s) 144. Dataset 132 may beaggregated over time, may be updated as human editor 126 submitsadditional compilation authoring data 128 to system 100, and may bestored in compilation authoring database 130.

Dataset 132 may be obtained from compilation authoring database 130 bysoftware code 110, executed by hardware processor 104 of computingplatform 102. In implementations in which compilation authoring database130 and software code 110 are co-located in system memory 106, hardwareprocessor 104 may execute software code 110 to access dataset 132directly. However, in implementations in which compilation authoringdatabase 130 is stored remotely from software code 110, hardwareprocessor 104 may execute software code 110 to obtain dataset 132 viacommunication network 108 and network communication links 118.

Flowchart 490 continues with generating, using trained machine learningmodel 112 and dataset 132, a compilation authoring template in anautomated process (action 492). It is noted that as defined in thepresent application, the feature “trained machine learning model 112”refers to a mathematical model for making future predictions based onpatterns learned from samples of data obtained from a set of trustedknown matches and known mismatches, known as “training data.” Variouslearning algorithms can be used to map correlations between input dataand output data. These correlations form the mathematical model that canbe used to make future predictions on new input data. Such a predictivemodel may include one or more logistic regression models, Bayesianmodels, or neural networks (NNs), for example.

According to various implementations of the present concepts, hardwareprocessor 104 of computing platform 102 executes software code 110 toprovide dataset 132 as input data to trained machine learning model 112,which in turn is configured to generate a compilation authoring templateimplementing an editorial style of human editor 126 learned from dataset132 in an automated process. Thus, based on the work of human editors,content compilation system 100 can advantageously generate contentcompilation templates that are pre-populated with weighting factors andother authoring parameters characteristic of each human editor'seditorial style. Those content compilation templates may then beutilized by software code 110 in an automated process for producingadditional content compilation(s) 144 for end-users 124 withoutrequiring further involvement in the authoring process by the humaneditors.

Accordingly, flowchart 490 can continue with producing, using end-userconsumption profile 122 a of end-user 124 a and the compilationauthoring template generated in action 492, another content compilationfor end-user 124 a (action 493). Action 493 may be performed by softwarecode 110, executed by hardware processor 104 of computing platform 102,in a manner analogous to that described above by reference to actions384, 385, and 386 of flowchart 380.

Flowchart 490 continues with computing another desirability scorepredicting the desirability of the content compilation produced inaction 493 to end-user 124 a (action 494). The desirability scorecomputation in action 494 may be performed by software code 110,executed by hardware processor 104 of computing platform 102, in amanner analogous to that described above by reference to action 387 offlowchart 380.

In some implementations, flowchart 490 may continue and conclude withproviding, when the desirability score computed in action 494 satisfiesa predetermined threshold, the content compilation produced in action493 to end-user 124 a (action 495). Action 495 may be performed bysoftware code 110, executed by hardware processor 104 of computingplatform 102, in a manner analogous to that described above by referenceto action 388 of flowchart 380. That is to say, action 495 may includepushing the content compilation produced in action 493 to end-user 124 awhen the desirability score computed in action 494 satisfies thepredetermined threshold.

In some implementations, hardware processor 104 may further executesoftware code 110 to improve the performance of content compilationsystem 100 through additional machine learning by trained machinelearning model 112 based on learned end-user tendencies. In thoseimplementations, flowchart 490 may continue with optionally receivingfeedback data 146 rating the actual desirability of the contentcompilation produced in action 493 to end-user 124 a (action 496). Asshown in FIG. 1 , feedback data 146 may be received by software code 110of content compilation system 100, executed by hardware processor 104,and via communication network 108 and network communication links 118.

In implementations in which flowchart 490 includes optional action 496,flowchart 490 can continue and conclude with optionally modifying, usingtrained machine learning model 112 and feedback data 146, thecompilation authoring template generated in action 492 to improve theautomated performance of content compilation system 100 (action 497).For example, software code 110, executed by hardware processor 104 ofcomputing platform 102, may utilize trained machine learning model 112to modify the compilation authoring template generated in action 492 bychanging one or more weighting factors or other authoring parameters soas to increase the user desirability score computed for contentcompilations produced using the modified compilation authoring template.

Referring to FIGS. 3 and 4 , it is emphasized that, in variousimplementations, actions 382-388, or actions 382-388 and 491-495, oractions 491-495, or actions 491-497, or 382-388 and actions 491-497 maybe performed in an automated process from which human involvement may beomitted.

Thus, the present application discloses systems and methods for curatingnarrative experiences through automated content compilation that addressand overcome the deficiencies in the conventional art. As disclosedabove, an automated content compilation system according to the presentnovel and inventive concepts produces a content compilation forend-user(s), and computes a desirability score predicting thedesirability of the content compilation to the end-user(s). Theautomated content compilation is performed by the system based on aconsumption profile of the end-user(s) and either content authoring datareceived through an editorial interface from a human editor other thanthe end-user(s), or based on a compilation authoring template generatedby a trained machine learning model of the system. When the desirabilityscore for the content compilation produced by the present systemsatisfies a predetermined threshold, the content compilation may bepushed or otherwise provided to the end-user(s) sua sponte, i.e.,proactively by the system, rather than in response to a request for thecontent compilation from the end user(s). Moreover, the disclosed systemcan further receive feedback data rating an actual desirability of thecontent compilation to the end-user(s), and modify, using the trainedmachine learning model and the feedback data, a compilation authoringtemplate used by the system to advantageously improve the automatedperformance by the content compilation system in the future.

From the above description it is manifest that various techniques can beused for implementing the concepts described in the present applicationwithout departing from the scope of those concepts. Moreover, while theconcepts have been described with specific reference to certainimplementations, a person of ordinary skill in the art would recognizethat changes can be made in form and detail without departing from thescope of those concepts. As such, the described implementations are tobe considered in all respects as illustrative and not restrictive. Itshould also be understood that the present application is not limited tothe particular implementations described herein, but manyrearrangements, modifications, and substitutions are possible withoutdeparting from the scope of the present disclosure.

1-20. (canceled) 21: A content compilation system comprising: acomputing platform having a hardware processor and a system memory; atrained machine learning model stored in the system memory; and asoftware code stored in the system memory; the hardware processorconfigured to execute the software code to: obtain a plurality ofcontent items from at least one content source; aggregate the pluralityof content items into one or more content subsets; generate, using thetrained machine learning model, a compilation authoring template in anautomated process; produce a content compilation, using the compilationauthoring template and the one or more content subsets; and provide thecontent compilation to one or more end-users. 22: The contentcompilation system of claim 21, wherein the content compilation isindividualized for the one or more end-users based on a profile of theone or more end-users. 23: The content compilation system of claim 21,wherein the content compilation is unique to each of the one or moreend-users. 24: The content compilation system of claim 21, wherein thehardware processor is further configured to execute the software codeto: determine a first desirability score predicting a first desirabilityof the content compilation to the one or more end-users. 25: The contentcompilation system of claim 24, wherein the first desirability scorecomprises an end-user relevance score. 26: The content compilationsystem of claim 24, wherein the hardware processor is further configuredto execute the software code to: determine a second desirability scorepredicting a second desirability of a second content compilation to theone or more end-users; and provide the second content compilation to theone or more end-users, when the second desirability score satisfies apredetermined threshold. 27: The content compilation system of claim 21,wherein the hardware processor is further configured to execute thesoftware code to: receive feedback data, wherein the feedback data ratesan actual desirability of the content compilation to the one or moreend-users; and modify, using the trained machine learning model and thefeedback data, the compilation authoring template to improve aperformance by the content compilation system. 28: A method for use by acontent compilation system including a computing platform having ahardware processor and a system memory storing a trained machinelearning model and a software code, the method comprising: obtaining, bythe software code executed by the hardware processor, a plurality ofcontent items from at least one content source; aggregating, by thesoftware code executed by the hardware processor, the plurality ofcontent items into one or more content subsets; generating, by thesoftware code executed by the hardware processor, using the trainedmachine learning model, a compilation authoring template in an automatedprocess; producing a content compilation, by the software code executedby the hardware processor, using the compilation authoring template andthe one or more content subsets; and providing, by the software codeexecuted by the hardware processor, the content compilation to one ormore end-users. 29: The method of claim 28, wherein the contentcompilation is individualized for the one or more end-users based on aprofile of the one or more end-users. 30: The method of claim 28,wherein the content compilation is unique to each of the one or moreend-users. 31: The method of claim 28, further comprising: determining,by the software code executed by the hardware processor, a firstdesirability score predicting a first desirability of the contentcompilation to the one or more end-users. 32: The method of claim 31,wherein the first desirability score comprises an end-user relevancescore. 33: The method of claim 31, further comprising: determining, bythe software code executed by the hardware processor, a seconddesirability score predicting a second desirability of a second contentcompilation to the one or more end-users; and providing the secondcontent compilation to the one or more end-users, by the software codeexecuted by the hardware processor, when the second desirability scoresatisfies a predetermined threshold. 34: The method of claim 28, furthercomprising: receiving, by the software code executed by the hardwareprocessor, feedback data, wherein the feedback data rates an actualdesirability of the content compilation to the one or more end-users;and modifying, by the software code executed by the hardware processor,using the trained machine learning model and the feedback data, thecompilation authoring template to improve a performance by the contentcompilation system.